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Tomography
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Imaging in Cancer Diagnosis: Second Edition
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Imaging in Cancer Diagnosis: Second Edition

A Special Issue of Tomography (ISSN 2379-139X) belonging to the section "Cancer Imaging".

Deadline for manuscript submissions: 25 June 2027 | Viewed by 324

Editor

Dr. Filippo Crimì

E-Mail Website
Guest Editor
Institute of Radiology, Department of Medicine—DIMED, University of Padua, 35128 Padua, Italy
Interests: MRI; PET; diagnostic imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the first edition of the Special Issue “Imaging in Cancer Diagnosis”, we are pleased to announce a second edition dedicated to recent advances in oncologic imaging and their potential impact on clinical practice.

In oncology, imaging is no longer just a supportive tool: it drives patient management from initial diagnosis through staging, treatment adaptation, and long-term follow-up. What is truly exciting about recent advances is how they allow us to move past simple visual tracking and extract quantitative imaging features.

The development of radiomics, artificial intelligence, and advanced image processing has expanded the role of imaging beyond visual assessment, although challenges in standardization, reproducibility, and clinical implementation remain. As technology continues to accelerate at a rapid pace, radiologists are no longer just traditional image readers.

We are becoming central to actively shaping the patient's entire therapeutic journey from the beginning.

The aim of this Special Issue is to provide an updated overview of current developments in cancer imaging and to highlight innovative approaches that may contribute to improving the diagnosis and management of oncologic diseases.

We welcome the submission of original research articles, review articles, technical contributions, and clinically relevant case reports related to these topics.

Dr. Filippo Crimì
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Tomography is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • CT
  • MRI
  • US
  • PET
  • cancer
  • diagnosis
  • imaging

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Related Special Issue

Published Papers (1 paper)

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Research

22 pages, 4344 KB  
Article
Radiomic Characterization of Breast Tissue from Breast CT Images Obtained with Synchrotron Beams
by Marlen Perez-Diaz, Ariel Fernandez-Pirez, Luca Brombal, Renata Longo, Anton Maksimenko, Caroline Kachana Pwamang, Stevan Vrbaski and Luigi Rigon
Tomography 2026, 12(9), 124; https://doi.org/10.3390/tomography12090124 - 29 Aug 2026
Viewed by 213
Abstract
Background/Objective: Radiomics offers a powerful, non-invasive approach for extracting quantitative features to predict lesion phenotypes. This study performs a radiomic characterization of breast tissue using synchrotron radiation breast computed tomography (SR-bCT). Method: Four mastectomy samples were imaged at the Australian Synchrotron (ANSTO) using [...] Read more.
Background/Objective: Radiomics offers a powerful, non-invasive approach for extracting quantitative features to predict lesion phenotypes. This study performs a radiomic characterization of breast tissue using synchrotron radiation breast computed tomography (SR-bCT). Method: Four mastectomy samples were imaged at the Australian Synchrotron (ANSTO) using five energies (25, 32, 35, 40, and 60 keV). From 10 slices per energy for each sample, 1546 regions of interest (ROIs) were extracted across four tissue subtypes: fatty, glandular, fibrous, and microcalcified. Using Pyradiomics, 93 features were initially calculated and then reduced to 34, eliminating highly correlated variables to reduce redundancies. Seven linear regression models and a discriminant analysis evaluated subtype tissue separation and radiomic characterization across individuals and combined samples and energies with a good fit (0.81 ≤ R ≤ 0.98). Results: The study identified six possible robust exploratory candidate features independent of sample variability and energy levels, whose mean values are significantly different among the four tissue subtypes (clusters, p < 0.001). The selected candidate features were 90th Percentile, Kurtosis, Skewness, GLCM IDM, GLDM LDE, and NGTDM Coarseness. These metrics successfully differentiated all tissue pairs (microcalcifications/fat, microcalcifications/gland, microcalcifications/fiber, fat/gland, fat/fiber and gland/fiber), with p < 0.05 inside the most general regression model and p < 0.0001 with the linear discriminant separation in clusters. Conclusions: Findings indicate that some first-order and second-order texture metrics reflecting global dependencies remain stable across experimental conditions. Conversely, fine-texture metrics are highly sensitive to sample energy changes, limiting their generalizability. These results align with successful biomarkers in mammography and validate the potential of radiomics in SR-bCT characterization. Full article
(This article belongs to the Special Issue Imaging in Cancer Diagnosis: Second Edition)
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